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Dream Server FAQ

Quick answers to common questions.

Looking for install/runtime troubleshooting? See TROUBLESHOOTING.md and INSTALL-TROUBLESHOOTING.md.

Hardware

What hardware do I need?

Lightweight (runs on anything):

  • GPU: Any (or CPU-only)
  • RAM: 4GB+
  • Storage: 15GB free
  • Model: Qwen3.5 2B (auto-selected)

Minimum (comfortable):

  • GPU: RTX 3060 12GB or RTX 4060 8GB
  • RAM: 32GB
  • Storage: 500GB NVMe SSD
  • CPU: Any modern quad-core

Recommended (comfortable daily use):

  • GPU: RTX 4070 Ti Super 16GB or RTX 4090 24GB
  • RAM: 64GB
  • Storage: 1TB NVMe SSD

Why these specs?

  • 12GB VRAM = 7B-14B models, basic tasks
  • 16GB VRAM = 32B models with reduced context
  • 24GB VRAM = 32B models with full context, voice pipeline
  • 48GB+ VRAM (2x 4090) = Multiple models, concurrent users

How much does a build cost?

Tier GPU Total Build What You Get
Entry RTX 3060 12GB $800-1,200 Basic chat, slow but works
Prosumer RTX 4070 Ti 16GB $2,000-3,000 Comfortable single-user
Pro RTX 4090 24GB $4,000-6,000 Fast, voice agents, 5-10 users
Enterprise 2x RTX 4090 $12,000-18,000 20-40 concurrent users

See HARDWARE-GUIDE.md for full breakdown.

What about electricity costs?

  • Idle: 50-100W (~$5-15/month)
  • Active inference: 300-450W per GPU
  • 24/7 heavy use: $30-80/month depending on rates

Still cheaper than cloud API bills at moderate usage.

Capabilities

What can Dream Server do?

Out of the box:

  • 💬 ChatGPT-style web interface (Open WebUI)
  • 🎤 Voice transcription (Whisper)
  • 🔊 Text-to-speech (Kokoro)
  • 📄 Document Q&A with RAG (Qdrant + embeddings)
  • 🔗 API integration (OpenAI-compatible endpoints)
  • 🤖 Agent workflows (n8n)

With voice profile:

  • 🎙️ Full voice agents (speak in, speak out)
  • Real-time conversations at <2s latency

With optional components:

  • 🔒 Privacy Shield (PII redaction proxy)
  • 🖼️ Image generation (if you add FLUX/SD)
  • 🔍 Local web search (SearXNG)

How fast is it?

Real benchmarks from our dual-4090 cluster:

Scenario Latency Concurrent Users
Single chat request ~1.4s 1
10 simultaneous chats ~1.5s 10
20 simultaneous chats ~1.6s 20
Voice agent (full round-trip) <2s 15-20 per GPU

Your results depend on hardware tier. Single 4090 ≈ half the concurrent capacity.

Is it as good as GPT-4 / Claude?

Honest answer: For most tasks, 32B local models are 80-90% as capable.

Where local wins:

  • Speed (no network latency)
  • Privacy (data never leaves your network)
  • Cost (no per-token fees)
  • Control (choose your model, tune prompts, no content filters)

Where cloud wins:

  • Cutting-edge reasoning (GPT-4, Claude 3.5)
  • Multimodal (vision, though Qwen-VL is catching up)
  • Zero maintenance

Our recommendation: Use local for daily work, cloud for edge cases.

Cost & ROI

How does cost compare to cloud APIs?

Example: 100,000 tokens/day usage

Option Monthly Cost Notes
OpenAI GPT-4 ~$300-600 Per-token billing
Claude API ~$200-400 Per-token billing
Dream Server $30-80 Electricity only (after hardware)

Break-even timeline:

  • Light use (~$50/mo API): 2-3 years
  • Medium use (~$200/mo API): 6-12 months
  • Heavy use (~$500+/mo API): 3-6 months

Plus: No usage caps, no rate limits, no surprise bills.

What about maintenance costs?

Time investment:

  • Initial setup: 1-2 hours with install wizard
  • Ongoing maintenance: ~30 min/month (updates, monitoring)
  • Model updates: Optional, 1-click when you want them

No paid support required for most users. Community Discord available.

Privacy & Security

Is it really private?

Yes, 100%. Your prompts never leave your local network.

  • No data sent to cloud providers
  • No logging by third parties
  • No training data contribution
  • Full GDPR/HIPAA compliance capability

Can I use it with sensitive data?

Yes. Common use cases:

  • Legal document review
  • Medical record analysis
  • Financial data processing
  • Internal company communications
  • Client confidential work

Optional: Add Privacy Shield for automatic PII redaction as an extra layer.

What about model security?

  • Models run in Docker containers (isolated)
  • No outbound network required after initial download
  • You control which models to run
  • Can air-gap the server if needed

Setup & Support

How hard is it to set up?

With install wizard: Under 1 hour for someone comfortable with terminal.

curl -fsSL https://raw.githubusercontent.com/Light-Heart-Labs/DreamServer/main/get-dream-server.sh | bash

The wizard:

  1. Detects your hardware
  2. Recommends configuration
  3. Downloads models
  4. Starts services
  5. Runs health checks

What if I'm not technical?

Options:

  1. Pre-configured hardware: We can ship ready-to-plug-in units
  2. Remote setup service: $200-500 depending on complexity
  3. Detailed guides: Step-by-step docs for common scenarios

How do I get updates?

./dream-cli update

That's it. Updates are optional — you control when to apply them.

Where do I get help?

  1. This documentation
  2. TROUBLESHOOTING.md for common issues
  3. GitHub Issues: https://github.com/Light-Heart-Labs/DreamServer/issues
  4. Discord community (link in README)

Comparisons

Dream Server vs Ollama?

Feature Dream Server Ollama
Web UI ✅ Built-in (Open WebUI) ❌ Separate install
Voice ✅ Full pipeline ❌ Not included
RAG ✅ Built-in ❌ Not included
n8n workflows ✅ Included ❌ Not included
One-command setup ✅ Yes ⚠️ Partial
Performance ✅ llama-server (faster) ⚠️ Ollama

Ollama is great for quick experiments. Dream Server is a complete production stack.

Dream Server vs LocalAI?

LocalAI is developer-focused. Dream Server is user-focused.

  • LocalAI: More flexibility, more configuration needed
  • Dream Server: Opinionated defaults, works out of box

Dream Server vs cloud APIs?

See "Cost & ROI" section above. TL;DR: Local is cheaper at scale, more private, but requires hardware investment.

Built by Light Heart Labs / The Collective